论文标题
使用深度转移学习的上皮卵巢癌全裂缝病理图像的分类
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning
论文作者
论文摘要
卵巢癌是女性生殖器官中最致命的癌症。上皮卵巢癌的主要组织学亚型$ 5 $，每种都有独特的形态，遗传和临床特征。目前，这些组织型是由病理学家对肿瘤全裂片图像（WSI）的微观检查确定的。糟糕的观察者协议（Cohen's Kappa $ 0.54 $ - $ 0.67 $）阻碍了此过程。我们利用了基于卷积神经网络（CNN）的\ textit {两}阶段深度转移学习算法和渐进式调整大小，以自动分类上皮卵巢癌WSIS。拟议的算法的平均准确性为$ 87.54 \％$，Cohen's Kappa的幻灯片分类为$ 305 $ WSIS $ 0.8106 $；在没有妇科特异性训练的情况下，执行比标准CNN和病理学家更好。
Ovarian cancer is the most lethal cancer of the female reproductive organs. There are $5$ major histological subtypes of epithelial ovarian cancer, each with distinct morphological, genetic, and clinical features. Currently, these histotypes are determined by a pathologist's microscopic examination of tumor whole-slide images (WSI). This process has been hampered by poor inter-observer agreement (Cohen's kappa $0.54$-$0.67$). We utilized a \textit{two}-stage deep transfer learning algorithm based on convolutional neural networks (CNN) and progressive resizing for automatic classification of epithelial ovarian carcinoma WSIs. The proposed algorithm achieved a mean accuracy of $87.54\%$ and Cohen's kappa of $0.8106$ in the slide-level classification of $305$ WSIs; performing better than a standard CNN and pathologists without gynecology-specific training.
